


'''simple docstring'''
import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()





def    a__			(   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__		)       ->      Optional[Any]:
    # Initialise PyTorch model
    UpperCAmelCase__     :						Dict						=							LxmertConfig.from_json_file(lowerCAmelCase__		)
    print(F"""Building PyTorch model from configuration: {config}"""		)
    UpperCAmelCase__     :						str						=							LxmertForPreTraining(lowerCAmelCase__		)

    # Load weights from tf checkpoint
    load_tf_weights_in_lxmert(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__		)

    # Save pytorch-model
    print(F"""Save PyTorch model to {pytorch_dump_path}"""		)
    torch.save(model.state_dict()    ,   lowerCAmelCase__		)


if __name__ == "__main__":
  UpperCamelCase__          = argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
  )
  parser.add_argument(
      '''--config_file''',
      default=None,
      type=str,
      required=True,
      help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
  )
  parser.add_argument(
      '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
  )
  UpperCamelCase__          = parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)



def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			return x if y == 0 else greatest_common_divisor(a__  ,	x % y)


def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			return (x * y) // greatest_common_divisor(a__  ,	a__)


def 					_UpperCAmelCase				(						a__ = 2_0):

			'''simple docstring'''



			a_						:						Any 							= 1
			for i in range(1  ,	n + 1):
						a_						:						Tuple 							= lcm(a__  ,	a__)
			return g


if __name__ == "__main__":
	print(F"""{solution() = }""")


